Multiple Regression: Approaches to Forecasting : A Tutorial
Published on: Jan, 25, 2011
Advanced techniques can be used when there is trend or seasonality, or when other factors (such as price discounts) must be considered.What is Multiple Regression?
- Analogous to single regression, but allows us to have multiple predictor variables:
- Y = a + b1*X1 + b2*X2 + b3*X3 …
- In most cases, 2 or 3 predictor variables should be plenty.
In this case, we have 24 months of data.
In addition to an apparent upward trend, we have price discount information and seasonality in the last two months of each year.
Let’s develop a multiple regression forecast model that considers all these factors…
h2. Resulting Forecast Model
Demand = 9117.08
+ 275.41(Time Period)
+ 2586.31(Seasonal Bump*)
*= 1 if seasonal bump is present; 0 otherwise
The multiple regression model does a decent job modeling past demand. By plugging in the appropriate time period and seasonality value (0 or 1) we can use it to forecast future demands.
Read the Supply Chain Management Professional Newsletter
Read the latest supply chain research, articles, and news as soon as we post them.